


import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     Any					=					[
			    'encoder.version',
			    'decoder.version',
			    'model.encoder.version',
			    'model.decoder.version',
			    'decoder.output_projection.weight',
			    '_float_tensor',
			    'encoder.embed_positions._float_tensor',
			    'decoder.embed_positions._float_tensor',
			]
			for k in ignore_keys:
						state_dict.pop(A__,      A__				)






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[Any]					=					emb.weight.shape
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					nn.Linear(A__,      A__,      bias=A__				)
			SCREAMING_SNAKE_CASE_		:     Dict					=					emb.weight.data
			return lin_layer






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					torch.load(A__,      map_location='cpu'				)
			SCREAMING_SNAKE_CASE_		:     Tuple					=					mam_aaa['args'] or mam_aaa['cfg']['model']
			SCREAMING_SNAKE_CASE_		:     Any					=					mam_aaa['model']
			remove_ignore_keys_(A__				)
			SCREAMING_SNAKE_CASE_		:     List[str]					=					state_dict['encoder.embed_tokens.weight'].shape[0]

			SCREAMING_SNAKE_CASE_		:     Dict					=					MaMaaaConfig(
			    vocab_size=A__,      max_position_embeddings=1_0_2_4,      encoder_layers=args.encoder_layers,      decoder_layers=args.decoder_layers,      encoder_attention_heads=args.encoder_attention_heads,      decoder_attention_heads=args.decoder_attention_heads,      encoder_ffn_dim=args.encoder_ffn_embed_dim,      decoder_ffn_dim=args.decoder_ffn_embed_dim,      d_model=args.encoder_embed_dim,      encoder_layerdrop=args.encoder_layerdrop,      decoder_layerdrop=args.decoder_layerdrop,      dropout=args.dropout,      attention_dropout=args.attention_dropout,      activation_dropout=args.activation_dropout,      activation_function='relu',      )

			SCREAMING_SNAKE_CASE_		:     int					=					state_dict['decoder.embed_tokens.weight']
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					MaMaaaForConditionalGeneration(A__				)
			model.model.load_state_dict(A__,      strict=A__				)
			SCREAMING_SNAKE_CASE_		:     int					=					make_linear_from_emb(model.model.shared				)

			return model


if __name__ == "__main__":
				lowerCAmelCase__			:      Optional[int]								=argparse.ArgumentParser()
				# Required parameters
				parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
				parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				lowerCAmelCase__			:      Optional[Any]								=parser.parse_args()
				lowerCAmelCase__			:      int								=convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
				model.save_pretrained(args.pytorch_dump_folder_path)










import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class 				_a				(   unittest.TestCase					):

			def      __snake_case       (self					)       ->   Optional[Any]:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()

			@property
			def      __snake_case       (self					)       ->   int:
						UpperCAmelCase_:		str							      =     1
						UpperCAmelCase_:		Optional[int]							      =     3
						UpperCAmelCase_:		Optional[Any]							      =     (32, 32)

						UpperCAmelCase_:		int							      =     floats_tensor((batch_size, num_channels) + sizes,   rng=random.Random(0					)					).to(SCREAMING_SNAKE_CASE_					)
						return image

			@property
			def      __snake_case       (self					)       ->   str:
						torch.manual_seed(0					)
						UpperCAmelCase_:		Optional[int]							      =     UNetaDConditionModel(
						    block_out_channels=(32, 64),   layers_per_block=2,   sample_size=32,   in_channels=4,   out_channels=4,   down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D"""),   up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D"""),   cross_attention_dim=32,   )
						return model

			@property
			def      __snake_case       (self					)       ->   Optional[int]:
						torch.manual_seed(0					)
						UpperCAmelCase_:		Union[str, Any]							      =     AutoencoderKL(
						    block_out_channels=[32, 64],   in_channels=3,   out_channels=3,   down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],   up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],   latent_channels=4,   )
						return model

			@property
			def      __snake_case       (self					)       ->   Tuple:
						torch.manual_seed(0					)
						UpperCAmelCase_:		List[Any]							      =     RobertaSeriesConfig(
						    hidden_size=32,   project_dim=32,   intermediate_size=37,   layer_norm_eps=1E-05,   num_attention_heads=4,   num_hidden_layers=5,   pad_token_id=1,   vocab_size=5006,   )
						return RobertaSeriesModelWithTransformation(SCREAMING_SNAKE_CASE_					)

			@property
			def      __snake_case       (self					)       ->   Union[str, Any]:
						def extract(*SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					):







									class 				_a				:

												def __init__(self					)       ->   Tuple:
															UpperCAmelCase_:		Any							      =     torch.ones([0]					)

												def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   List[Any]:
															self.pixel_values.to(SCREAMING_SNAKE_CASE_					)
															return self






									return Out()

						return extract

			def      __snake_case       (self					)       ->   int:
						UpperCAmelCase_:		List[str]							      =     """cpu"""  # ensure determinism for the device-dependent torch.Generator
						UpperCAmelCase_:		Union[str, Any]							      =     self.dummy_cond_unet
						UpperCAmelCase_:		Any							      =     PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[Any]							      =     self.dummy_vae
						UpperCAmelCase_:		int							      =     self.dummy_text_encoder
						UpperCAmelCase_:		int							      =     XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""					)
						UpperCAmelCase_:		Tuple							      =     77

						UpperCAmelCase_:		Tuple							      =     self.dummy_image.to(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		str							      =     init_image / 2 + 0.5

						# make sure here that pndm scheduler skips prk
						UpperCAmelCase_:		List[Any]							      =     AltDiffusionImgaImgPipeline(
						    unet=SCREAMING_SNAKE_CASE_,   scheduler=SCREAMING_SNAKE_CASE_,   vae=SCREAMING_SNAKE_CASE_,   text_encoder=SCREAMING_SNAKE_CASE_,   tokenizer=SCREAMING_SNAKE_CASE_,   safety_checker=SCREAMING_SNAKE_CASE_,   feature_extractor=self.dummy_extractor,   )
						UpperCAmelCase_:		Dict							      =     VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,   do_normalize=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[int]							      =     alt_pipe.to(SCREAMING_SNAKE_CASE_					)
						alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		str							      =     """A painting of a squirrel eating a burger"""
						UpperCAmelCase_:		List[str]							      =     torch.Generator(device=SCREAMING_SNAKE_CASE_					).manual_seed(0					)
						UpperCAmelCase_:		Optional[int]							      =     alt_pipe(
						    [prompt],   generator=SCREAMING_SNAKE_CASE_,   guidance_scale=6.0,   num_inference_steps=2,   output_type="""np""",   image=SCREAMING_SNAKE_CASE_,   )

						UpperCAmelCase_:		List[str]							      =     output.images

						UpperCAmelCase_:		Optional[Any]							      =     torch.Generator(device=SCREAMING_SNAKE_CASE_					).manual_seed(0					)
						UpperCAmelCase_:		Any							      =     alt_pipe(
						    [prompt],   generator=SCREAMING_SNAKE_CASE_,   guidance_scale=6.0,   num_inference_steps=2,   output_type="""np""",   image=SCREAMING_SNAKE_CASE_,   return_dict=SCREAMING_SNAKE_CASE_,   )[0]

						UpperCAmelCase_:		List[str]							      =     image[0, -3:, -3:, -1]
						UpperCAmelCase_:		str							      =     image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 32, 32, 3)
						UpperCAmelCase_:		Tuple							      =     np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9]					)

						assert np.abs(image_slice.flatten() - expected_slice					).max() < 5E-3
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 5E-3

			@unittest.skipIf(torch_device != """cuda""",   """This test requires a GPU"""					)
			def      __snake_case       (self					)       ->   str:
						UpperCAmelCase_:		List[Any]							      =     self.dummy_cond_unet
						UpperCAmelCase_:		List[Any]							      =     PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Any							      =     self.dummy_vae
						UpperCAmelCase_:		Optional[Any]							      =     self.dummy_text_encoder
						UpperCAmelCase_:		Dict							      =     XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""					)
						UpperCAmelCase_:		Union[str, Any]							      =     77

						UpperCAmelCase_:		Tuple							      =     self.dummy_image.to(SCREAMING_SNAKE_CASE_					)

						# put models in fp16
						UpperCAmelCase_:		Optional[Any]							      =     unet.half()
						UpperCAmelCase_:		Any							      =     vae.half()
						UpperCAmelCase_:		str							      =     bert.half()

						# make sure here that pndm scheduler skips prk
						UpperCAmelCase_:		Optional[int]							      =     AltDiffusionImgaImgPipeline(
						    unet=SCREAMING_SNAKE_CASE_,   scheduler=SCREAMING_SNAKE_CASE_,   vae=SCREAMING_SNAKE_CASE_,   text_encoder=SCREAMING_SNAKE_CASE_,   tokenizer=SCREAMING_SNAKE_CASE_,   safety_checker=SCREAMING_SNAKE_CASE_,   feature_extractor=self.dummy_extractor,   )
						UpperCAmelCase_:		Optional[Any]							      =     VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,   do_normalize=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Any							      =     alt_pipe.to(SCREAMING_SNAKE_CASE_					)
						alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Any							      =     """A painting of a squirrel eating a burger"""
						UpperCAmelCase_:		Optional[Any]							      =     torch.manual_seed(0					)
						UpperCAmelCase_:		Optional[int]							      =     alt_pipe(
						    [prompt],   generator=SCREAMING_SNAKE_CASE_,   num_inference_steps=2,   output_type="""np""",   image=SCREAMING_SNAKE_CASE_,   ).images

						assert image.shape == (1, 32, 32, 3)

			@unittest.skipIf(torch_device != """cuda""",   """This test requires a GPU"""					)
			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		Optional[Any]							      =     load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/img2img/sketch-mountains-input.jpg"""					)
						# resize to resolution that is divisible by 8 but not 16 or 32
						UpperCAmelCase_:		List[Any]							      =     init_image.resize((760, 504)					)

						UpperCAmelCase_:		Tuple							      =     """BAAI/AltDiffusion"""
						UpperCAmelCase_:		Tuple							      =     AltDiffusionImgaImgPipeline.from_pretrained(
						    SCREAMING_SNAKE_CASE_,   safety_checker=SCREAMING_SNAKE_CASE_,   )
						pipe.to(SCREAMING_SNAKE_CASE_					)
						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_					)
						pipe.enable_attention_slicing()

						UpperCAmelCase_:		Tuple							      =     """A fantasy landscape, trending on artstation"""

						UpperCAmelCase_:		Optional[int]							      =     torch.manual_seed(0					)
						UpperCAmelCase_:		int							      =     pipe(
						    prompt=SCREAMING_SNAKE_CASE_,   image=SCREAMING_SNAKE_CASE_,   strength=0.7_5,   guidance_scale=7.5,   generator=SCREAMING_SNAKE_CASE_,   output_type="""np""",   )
						UpperCAmelCase_:		Dict							      =     output.images[0]

						UpperCAmelCase_:		Tuple							      =     image[255:258, 383:386, -1]

						assert image.shape == (504, 760, 3)
						UpperCAmelCase_:		str							      =     np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0]					)

						assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2







@slow
@require_torch_gpu
class 				_a				(   unittest.TestCase					):

			def      __snake_case       (self					)       ->   int:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()

			def      __snake_case       (self					)       ->   Union[str, Any]:
						UpperCAmelCase_:		Optional[Any]							      =     load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/img2img/sketch-mountains-input.jpg"""					)
						UpperCAmelCase_:		Optional[Any]							      =     init_image.resize((768, 512)					)
						UpperCAmelCase_:		Union[str, Any]							      =     load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"""					)

						UpperCAmelCase_:		List[str]							      =     """BAAI/AltDiffusion"""
						UpperCAmelCase_:		int							      =     AltDiffusionImgaImgPipeline.from_pretrained(
						    SCREAMING_SNAKE_CASE_,   safety_checker=SCREAMING_SNAKE_CASE_,   )
						pipe.to(SCREAMING_SNAKE_CASE_					)
						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_					)
						pipe.enable_attention_slicing()

						UpperCAmelCase_:		Dict							      =     """A fantasy landscape, trending on artstation"""

						UpperCAmelCase_:		int							      =     torch.manual_seed(0					)
						UpperCAmelCase_:		int							      =     pipe(
						    prompt=SCREAMING_SNAKE_CASE_,   image=SCREAMING_SNAKE_CASE_,   strength=0.7_5,   guidance_scale=7.5,   generator=SCREAMING_SNAKE_CASE_,   output_type="""np""",   )
						UpperCAmelCase_:		Dict							      =     output.images[0]

						assert image.shape == (512, 768, 3)
						# img2img is flaky across GPUs even in fp32, so using MAE here
						assert np.abs(expected_image - image					).max() < 1E-2






